It is generally well known that precooling of buildings can lead to significant savings of energy consumption. It is easier to generate the cooling load during the night when the outdoor air is cold. Furthermore, natural ventilation can be used. Additionally, some utility companies offer tariffs with prices varying with the time of day that can make the pre-cooling even more efficient. Of course, the capability of precooling is given also by the properties of building such as its insulation and capacity.
Model predictive control offers a variety of methods to deal with given problem. It consists in determination of actions (intensity of chillers' and fans' operation) so the expected loss is minimal. However, the model typically works with many simplifications. Moreover, the weather forecast is affected by an error, typically non trivial. Thus, blind use of the optimized actions could lead to excesses from the comfort limits. This could have for the customer critical consequences like loss of buyers in the case of retail centers, or tenants in the case of residential buildings.